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Key feature identification for recognition of activities performed by a smart-home resident

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Abstract

Activity recognition is beneficial for continuous health monitoring of smart-home residents, such as patients and elderly people, living in the privacy of their home. We propose an activity recognition approach apposite for a smart home environment. The observations are obtained through multiple sensors deployed at different locations within a smart home. The activities are represented by the features selected from the received observations. The inconsistent order of performing the activities, infrequent occurrences and the presence of overlapping activities make it challenging to select the features with high class representative ability and inter-class discriminative qualities. We select the key features locally within each activity class, which is least affected by the order of performance and the occurrence of other activities. Next, for association of activities, we solve the existing multi-class problem through a specifically designed binary classification with ranking solution, which learns on the correct and incorrect assignments of activities. A comparison of proposed approach with existing methods in terms of recognition accuracy is presented on publicly available ‘Kasteren’ and ‘CASAS’ datasets, representing a range of overlapping and well separated activities of daily life. Our approach tailored towards a smart home environment demonstrates a better accuracy than existing methods.

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References

  • Alemdar H, Kasteren TV, Niessen M, Merentitis A, Ersoy C (2014) A unified model for human behavior modeling using a hierarchy with a variable number of states. In: Proceedings of IEEE international conference on pattern recognition, Stockholm, pp 3804–3809

  • Andre D, Wolf DL (2007) Recent advances in free-living physical activity monitoring: a review. J Diabetes Sci Technol 1(5):760–767

    Article  Google Scholar 

  • Atallah L, Yang GZ (2009) The use of pervasive sensing for behavior profiling—a survey. Pervasive Mob Comput 5(5):447–464

    Article  Google Scholar 

  • Avci U, Passerini A (2014) Improving activity recognition by segmental pattern mining. IEEE Trans Knowl Data Eng 26(4):889–902

    Article  Google Scholar 

  • Azkune G, Almeida A, de Ipia DL, Chen L (2015) Extending knowledge-driven activity models through data-driven learning techniques. Expert Syst Appl 42(6):3115–3128

    Article  Google Scholar 

  • Bao L, Intille SS (2004) Activity recognition from user-annotated acceleration data. In: Proceedings of international conference on pervasive computing, Vienna, pp 1–17

  • Bouwstra S, Chen W, Feijs LMG, Bambang-Oetomo S (2009) Smart jacket design for neonatal monitoring with wearable sensors. In: Proceedings of IEEE international workshop on wearable and implantable body sensor networks, California, pp 162–167

  • Chapelle O, Keerthi SS (2010) Efficient algorithms for ranking with SVMs. Inf Retr 13(3):201–215

    Article  Google Scholar 

  • Chen L, Hoey J, Chris N, Cook DJ, Yu Z (2010) Sensor-based activity recognition. IEEE Trans Syst Man Cybern Part C Appl Rev 42(6):790–808

    Article  Google Scholar 

  • Chen C, Das B, Cook DJ (2010) A data mining framework for activity recognition in smart environments. In: Proceedings of IEEE international conference on intelligent environments, Kuala Lumpur, pp 80–83

  • Chen L, Nugent C, Wang H (2012) A knowledge-driven approach to activity recognition in smart homes. IEEE Trans Knowl Data Eng 24(6):961–974

    Article  Google Scholar 

  • Chikhaoui B, Wang S, Pigot H (2012) Activity discovery and recognition by combining sequential patterns and latent dirichlet allocation. Pervasive Mob Comput 8(6):845–862

    Article  Google Scholar 

  • Chinellato E, Hogg DC, Cohn AG (2016) Feature space analysis for human activity recognition in smart environments. In: Proceedings of IEEE international conference on intelligent environments, London, pp 194–197

  • Danaei-mehr H, Polat H, Cetin A (2016) Resident activity recognition in smart homes by using artificial neural networks. In: Proceedings of IEEE international conference on smart grid congress and fair (ICSG), Istanbul

  • Dhirl CS, Iqbal N, Lee SY (2007) Efficient feature selection based on information gain criterion for face recognition. In: Proceedings of IEEE international conference on information acquisition, Seogwipo-si, pp 523–527

  • Ermes M, Parkka J, Mantyjarvi J, Korhonen I (2011) Detection of daily activities and sports with wearable sensors in controlled and uncontrolled conditions. IEEE Trans Inf Technol Biomed 12(1):20–26

    Article  Google Scholar 

  • Fahad LG, Tahir SF, Rajarajan M (2014) Activity recognition in smart homes using clustering based classification. In: Proceedings of IEEE international conference on pattern recognition, Stockholm, pp 1348–1353

  • Fahad LG, Ali A, Rajarajan M (2015a) Learning models for activity recognition in smart homes. In: Proceedings of international conference on information science and applications, Pattaya

  • Fahad LG, Khan A, Rajarajan M (2015b) Activity recognition in smart homes with self verification of assignments. Neurocomputing 149:1286–1298

    Article  Google Scholar 

  • Fahad LG, Tahir SF, Rajarajan M (2015c) Key features identification for activity recognition in smart homes. In: Proceedings of IEEE international conference on communications, London

  • Fang H, He L, Si H, Liu P, Xie X (2014) Human activity recognition based on feature selection in smart home using back-propagation algorithm. ISA Trans 53(5):1629–1638

    Article  Google Scholar 

  • Fleury A, Vacher M, Noury N (2010) Svm-based multimodal classification of activities of daily living in health smart homes: sensors, algorithms, and first experimental results. IEEE Trans Inf Technol Biomed 14(2):274–283

    Article  Google Scholar 

  • Forkan ARM, Khalil I, Tari Z, Foufou S, Bouras A (2015) A context-aware approach for long-term behavioural change detection and abnormality prediction in ambient assisted living. Pattern Recognit 48(3):628–641

    Article  Google Scholar 

  • Forman G (2003) An extensive empirical study of feature selection metrics for text classification. J Mach Learn Res 3:1289–1305

    MATH  Google Scholar 

  • Garcia CG, Nunez-Valdez ER, Garcia-Diaz V, G-Bustelo BCP, Lovelle JMC (2018) A review of artificial intelligence in the internet of things. Int J Interact Multimed Artif Intell 4(3):7–10

    Google Scholar 

  • Gu T, Wang L, Chen H, Tao X, Lu J (2011) Recognizing multiuser activities using wireless body sensor networks. IEEE Trans Mob Comput 10(11):1618–1631

    Article  Google Scholar 

  • Hoey J, Plotz T, Jackson D, Monk A, Pham C, Olivier P (2011) Rapid specification and automated generation of prompting systems to assist people with dementia. Pervasive Mob Comput 7(3):299–318

    Article  Google Scholar 

  • Hoque E, Stankovic J (2012) Aalo: activity recognition in smart homes using active learning in the presence of overlapped activities. In: Proceeding of IEEE international conference on pervasive computing technologies for healthcare, San Diego, pp 139–146

  • Huang PC, Lee SS, Kuo YH, Lee KR (2010) A flexible sequence alignment approach on pattern mining and matching for human activity recognition. Exp Syst Appl 37(1):298–306

    Article  Google Scholar 

  • Jalal A, Kamal S (2018) Improved behavior monitoring and classification using cues parameters extraction from camera array images. Int J Interact Multimed Artif Intell 5(7):1–8

    Google Scholar 

  • Kasteren TV, Noulas A, Englebienne G, Krose B (2008) Accurate activity recognition in a home setting. In: Proceeding of international conference on ubiquitous computing, Seoul, pp 1–9

  • Kasteren TV, Englebienne G, Krose B (2011) Hierarchical activity recognition using automatically clustered actions. In: Proceedings of international conference on ambient intelligence, Amsterdam, pp 82–91

  • Kushwah A, Kumar S, Hegde RM (2015) Multi-sensor data fusion methods for indoor activity recognition using temporal evidence theory. Pervasive Mob Comput 21(1):19–29

    Article  Google Scholar 

  • Lu CH, Ho YC, Chen YH, Fu LC (2013) Hybrid user-assisted incremental model adaptation for activity recognition in a dynamic smart-home environment. IEEE Trans Hum Mach Syst 43(5):421–436

    Article  Google Scholar 

  • Lu L, Zhan L, Yi-Ju CQ (2017) Activity recognition in smart homes. Multimed Tools Appl 76(22):2420324220

    Google Scholar 

  • Mckeever S, Ye J, Coyle L, Bleakley C, Dobson S (2010) Activity recognition using temporal evidence theory. J Ambient Intell Smart Environ 2(3):253–269

    Article  Google Scholar 

  • Mshali H, Lemlouma T, Magoni D (2018) Adaptive monitoring system for e-health smart homes. Pervasive Mob Comput 43:1–19

    Article  Google Scholar 

  • Nazerfard E, Das B, Holder LB, Cook DJ (2010) Conditional random fields for activity recognition in smart environments. In: Proceedings of ACM International Health Informatics Symposium, Washington, pp 282–286

  • Okeyo G, Chen L, Wang H (2014) Combining ontological and temporal formalisms for composite activity modeling and recognition in smart homes. Fut Gen Comput Syst 39:29–43

    Article  Google Scholar 

  • Ordonez FJ, Iglesias JA, de Toledo P, Ledezma A, Sanchis A (2013) Online activity recognition using evolving classifiers. Exp Syst Appl 40(4):1248–1255

    Article  Google Scholar 

  • Peng H, Long F, Ding C (2005) Feature selection based on mutual information criteria of max-dependency, max-relevance, and min-redundancy. IEEE Trans Pattern Anal Mach Intell 27(8):1226–1238

    Article  Google Scholar 

  • Rashidi P, Cook DJ (2009) Keeping the resident in the loop: adapting the smart home to the user. IEEE Trans Syst Man Cybern Part A 39(5):949–959

    Article  Google Scholar 

  • Rashidi P, Cook DJ, Holder LB, Schmitter-Edgecombe M (2011) Discovering activities to recognize and track in a smart environment. IEEE Trans Knowl Data Eng 23(4):527–539

    Article  Google Scholar 

  • Rialle V, Duchene F, Noury N, Bajolle L, Demongeot J (2002) Health smart home: information technology for patients at home. Telemed J e-Health 8(4):395–409

    Article  Google Scholar 

  • Riboni D, Pareschi L, Radaelli L, Bettini C (2011) Is ontology-based activity recognition really effective? IEEE international workshops on pervasive computing and communications, Seattle, pp 427–431

  • Rieping K, Englebienne G, Krose B (2014) Behavior analysis of elderly using topic models. Pervasive Mob Comput 15:181–199

    Article  Google Scholar 

  • Rifkin RM, Klautau A (2004) In defense of one-vs-all classification. J Mach Learn Res 5:101–141

    MathSciNet  MATH  Google Scholar 

  • Sebbak F, Chibani A, Amirat Y, Mokhtari A, Benhammadi F (2013) An evidential fusion approach for activity recognition in ambient intelligence environments. Robot Auton Syst 61(11):1235–1245

    Article  Google Scholar 

  • Shin JH, Lee B, Park KS (2011) Detection of abnormal living patterns for elderly living alone using support vector data description. IEEE Trans Inf Technol Biomed 15(3):438–448

    Article  Google Scholar 

  • Singla G, Cook DJ, Schmitter-edgecombe M (2008) Incorporating temporal reasoning into activity recognition for smart home residents. In: Proceedings of the AAAI workshop on spatial and temporal reasoning, Chicago, pp 53–61

  • Sorensen S, Duberstein P, Gill D, Pinquart M (2006) Dementia care: mental health effects, intervention strategies, and clinical implications. Lancet Neurol 5(11):961–973

    Article  Google Scholar 

  • Stikic M, Larlus D, Ebert S, Schiele B (2011) Weakly supervised recognition of daily life activities with wearable sensors. IEEE Trans Pattern Anal Mach Intell 33(12):2521–2537

    Article  Google Scholar 

  • Tapia EM, Intille SS, Larson K (2004) Activity recognition in the home using simple and ubiquitous sensors. Pervasive Comput 3001:158–175

    Article  Google Scholar 

  • Tolstikov A, Hong X, Biswas J, Nugent C, Chen L, Parente G (2011) Comparison of fusion methods based on dst and dbn in human activity recognition. J Control Theory Appl 9(1):18–27

    Article  Google Scholar 

  • Wang Z, Jiang M, Hu Y, Li H (2011) An incremental learning method based on probabilistic neural networks and adjustable fuzzy clustering for human activity recognition by using wearable sensors. IEEE Trans Inf Technol Biomed 16(4):691–699

    Article  Google Scholar 

  • Zhang M, Sawchuk AA (2011) A feature selection-based framework for human activity recognition using wearable multimodal sensors. In: Proceedings of the international conference on body area networks, Beijing

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Authors and Affiliations

  1. Department of Computer Science, Air University, Islamabad, Pakistan

    Syed Fahad Tahir & Kashif Kifayat

  2. Department of Computer Science, National University of Computer and Emerging Sciences, Islamabad, Pakistan

    Labiba Gillani Fahad

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  1. Syed Fahad Tahir
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  2. Labiba Gillani Fahad
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  3. Kashif Kifayat
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Correspondence to Syed Fahad Tahir.

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Tahir, S.F., Fahad, L.G. & Kifayat, K. Key feature identification for recognition of activities performed by a smart-home resident. J Ambient Intell Human Comput 11, 2105–2115 (2020). https://doi.org/10.1007/s12652-019-01236-y

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